Organic Rankine cycle (ORC) is one of the most promising technologies for medium and low temperature thermal energy-electric energy conversion

and has received more and more attention in recent years. Working fluid is the carrier for energy transport or conversion in the ORC. Because of the diversity of the heat source and working substances

the screening of working fluids and the optimization of the system are very important to improve the comprehensive performance of the ORC. Accurate prediction of working fluid properties is significant for the accurate prediction and optimization of the ORC performance. Based on the artificial neural network and group contribution method (ANN-GCM)

a prediction method for the ORC performance is presented. A group table covering 11 groups established

7958 of data are derived from REFPROP for ANN training

obtaining the correlation of the energy transform and entropy difference in DRC. The performance of the ORC is tested by using 21 common refrigerants in 1584 working conditions. The error of predicting the thermal efficiency

output power

and exergetic efficiency of the ORC system with the experimental data is 1.01%

1.02% and 1.61%. Comparing with the traditional method

the prediction accuracy is significantly improved.